Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3699
Missing cells6761
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory631.5 B

Variable types

Categorical10
Text3
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 4 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with super_built_up_areaHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (55.6%) Imbalance
facing has 1053 (28.5%) missing values Missing
super_built_up_area has 1823 (49.3%) missing values Missing
built_up_area has 1992 (53.9%) missing values Missing
carpet_area has 1821 (49.2%) missing values Missing
area is highly skewed (γ1 = 29.80647345) Skewed
built_up_area is highly skewed (γ1 = 40.84413146) Skewed
carpet_area is highly skewed (γ1 = 24.37203746) Skewed
floorNum has 130 (3.5%) zeros Zeros
luxury_score has 470 (12.7%) zeros Zeros

Reproduction

Analysis started2025-04-04 06:21:11.044156
Analysis finished2025-04-04 06:21:26.530576
Duration15.49 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size250.1 KiB
flat
2824 
house
875 

Length

Max length5
Median length4
Mean length4.2365504
Min length4

Characters and Unicode

Total characters15671
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2824
76.3%
house 875
 
23.7%

Length

2025-04-04T06:21:26.635473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T06:21:26.714669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
flat 2824
76.3%
house 875
 
23.7%

Most occurring characters

ValueCountFrequency (%)
f 2824
18.0%
l 2824
18.0%
a 2824
18.0%
t 2824
18.0%
h 875
 
5.6%
o 875
 
5.6%
u 875
 
5.6%
s 875
 
5.6%
e 875
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15671
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2824
18.0%
l 2824
18.0%
a 2824
18.0%
t 2824
18.0%
h 875
 
5.6%
o 875
 
5.6%
u 875
 
5.6%
s 875
 
5.6%
e 875
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 15671
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2824
18.0%
l 2824
18.0%
a 2824
18.0%
t 2824
18.0%
h 875
 
5.6%
o 875
 
5.6%
u 875
 
5.6%
s 875
 
5.6%
e 875
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2824
18.0%
l 2824
18.0%
a 2824
18.0%
t 2824
18.0%
h 875
 
5.6%
o 875
 
5.6%
u 875
 
5.6%
s 875
 
5.6%
e 875
 
5.6%
Distinct680
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size295.6 KiB
2025-04-04T06:21:27.025364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.846133
Min length1

Characters and Unicode

Total characters62297
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique310 ?
Unique (%)8.4%

Sample

1st rowambience lagoon
2nd rowcapital residences 360
3rd rowumang winter hills
4th rowrof ananda
5th rowshree vardhman flora
ValueCountFrequency (%)
independent 506
 
5.2%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.5%
heights 134
 
1.4%
Other values (784) 7513
77.4%
2025-04-04T06:21:27.519655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6768
 
10.9%
6012
 
9.7%
a 5874
 
9.4%
n 4215
 
6.8%
r 4184
 
6.7%
i 3849
 
6.2%
t 3739
 
6.0%
s 3482
 
5.6%
l 2943
 
4.7%
o 2759
 
4.4%
Other values (31) 18472
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55740
89.5%
Space Separator 6012
 
9.7%
Decimal Number 527
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6768
12.1%
a 5874
 
10.5%
n 4215
 
7.6%
r 4184
 
7.5%
i 3849
 
6.9%
t 3739
 
6.7%
s 3482
 
6.2%
l 2943
 
5.3%
o 2759
 
4.9%
d 2521
 
4.5%
Other values (16) 15406
27.6%
Decimal Number
ValueCountFrequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
0 13
 
2.5%
9 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6012
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55740
89.5%
Common 6557
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6768
12.1%
a 5874
 
10.5%
n 4215
 
7.6%
r 4184
 
7.5%
i 3849
 
6.9%
t 3739
 
6.7%
s 3482
 
6.2%
l 2943
 
5.3%
o 2759
 
4.9%
d 2521
 
4.5%
Other values (16) 15406
27.6%
Common
ValueCountFrequency (%)
6012
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 13
 
0.2%
9 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6768
 
10.9%
6012
 
9.7%
a 5874
 
9.4%
n 4215
 
6.8%
r 4184
 
6.7%
i 3849
 
6.2%
t 3739
 
6.0%
s 3482
 
5.6%
l 2943
 
4.7%
o 2759
 
4.4%
Other values (31) 18472
29.7%

sector
Text

Distinct98
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size266.9 KiB
2025-04-04T06:21:27.774971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.8934847
Min length5

Characters and Unicode

Total characters32897
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsector 24
2nd rowsector 70
3rd rowsector 77
4th rowsector 95
5th rowsector 90
ValueCountFrequency (%)
sector 3473
48.3%
sohna 166
 
2.3%
37 115
 
1.6%
85 108
 
1.5%
102 107
 
1.5%
70 104
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
65 87
 
1.2%
Other values (90) 2748
38.2%
2025-04-04T06:21:28.142291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 3673
11.2%
o 3639
11.1%
r 3533
10.7%
e 3507
10.7%
3491
10.6%
c 3473
10.6%
t 3473
10.6%
1 1080
 
3.3%
0 801
 
2.4%
8 778
 
2.4%
Other values (18) 5449
16.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22134
67.3%
Decimal Number 7272
 
22.1%
Space Separator 3491
 
10.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 3673
16.6%
o 3639
16.4%
r 3533
16.0%
e 3507
15.8%
c 3473
15.7%
t 3473
15.7%
a 304
 
1.4%
n 200
 
0.9%
h 184
 
0.8%
m 34
 
0.2%
Other values (7) 114
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 1080
14.9%
0 801
11.0%
8 778
10.7%
9 764
10.5%
6 742
10.2%
7 686
9.4%
2 680
9.4%
3 632
8.7%
5 623
8.6%
4 486
6.7%
Space Separator
ValueCountFrequency (%)
3491
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22134
67.3%
Common 10763
32.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 3673
16.6%
o 3639
16.4%
r 3533
16.0%
e 3507
15.8%
c 3473
15.7%
t 3473
15.7%
a 304
 
1.4%
n 200
 
0.9%
h 184
 
0.8%
m 34
 
0.2%
Other values (7) 114
 
0.5%
Common
ValueCountFrequency (%)
3491
32.4%
1 1080
 
10.0%
0 801
 
7.4%
8 778
 
7.2%
9 764
 
7.1%
6 742
 
6.9%
7 686
 
6.4%
2 680
 
6.3%
3 632
 
5.9%
5 623
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32897
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 3673
11.2%
o 3639
11.1%
r 3533
10.7%
e 3507
10.7%
3491
10.6%
c 3473
10.6%
t 3473
10.6%
1 1080
 
3.3%
0 801
 
2.4%
8 778
 
2.4%
Other values (18) 5449
16.6%

price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)12.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5300081
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-04-04T06:21:28.274059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.515
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9782314
Coefficient of variation (CV)1.1771628
Kurtosis14.913075
Mean2.5300081
Median Absolute Deviation (MAD)0.725
Skewness3.2757867
Sum9315.49
Variance8.8698625
MonotonicityNot monotonic
2025-04-04T06:21:28.405324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 58
 
1.6%
0.95 54
 
1.5%
2 53
 
1.4%
1.6 48
 
1.3%
Other values (463) 3076
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 9
0.2%
0.23 2
 
0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2662
Distinct (%)72.3%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13931.311
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-04-04T06:21:28.540384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4706.45
Q16812.25
median9013.5
Q313877.25
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7065

Descriptive statistics

Standard deviation23315.369
Coefficient of variation (CV)1.6735948
Kurtosis182.72286
Mean13931.311
Median Absolute Deviation (MAD)2796.5
Skewness11.285294
Sum51295086
Variance5.4360645 × 108
MonotonicityNot monotonic
2025-04-04T06:21:28.681006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 18
 
0.5%
12500 14
 
0.4%
11111 13
 
0.4%
22222 13
 
0.4%
6666 13
 
0.4%
7500 12
 
0.3%
8333 12
 
0.3%
33333 11
 
0.3%
Other values (2652) 3530
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1767
Distinct (%)48.0%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2886.8874
Minimum45.000138
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-04-04T06:21:28.828762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum45.000138
5-th percentile510.2173
Q11225
median1728.0258
Q32300
95-th percentile4277.55
Maximum875000
Range874955
Interquartile range (IQR)1075

Descriptive statistics

Standard deviation23101.809
Coefficient of variation (CV)8.0023244
Kurtosis947.11065
Mean2886.8874
Median Absolute Deviation (MAD)530.02585
Skewness29.806473
Sum10629519
Variance5.336936 × 108
MonotonicityNot monotonic
2025-04-04T06:21:28.958593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 52
 
1.4%
1950 42
 
1.1%
2000 33
 
0.9%
1350 29
 
0.8%
1800 22
 
0.6%
2400 22
 
0.6%
2150 21
 
0.6%
1900 20
 
0.5%
1300 20
 
0.5%
1550 19
 
0.5%
Other values (1757) 3402
92.0%
ValueCountFrequency (%)
45.00013846 1
 
< 0.1%
50 4
0.1%
55.000055 1
 
< 0.1%
56.00016291 1
 
< 0.1%
57.0000855 1
 
< 0.1%
60.00006486 1
 
< 0.1%
60.00016552 1
 
< 0.1%
61 1
 
< 0.1%
67.00012707 1
 
< 0.1%
67.00035471 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517.2414 1
< 0.1%
98977.94513 1
< 0.1%
82781.45695 1
< 0.1%
65517 2
0.1%
65261.04418 1
< 0.1%
58228 1
< 0.1%
Distinct2367
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size430.3 KiB
2025-04-04T06:21:29.322424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.112733
Min length12

Characters and Unicode

Total characters200163
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1861 ?
Unique (%)50.3%

Sample

1st rowsuper built up area 3200(297.29 sq.m.)carpet area: 3156 sq.ft. (293.2 sq.m.)
2nd rowsuper built up area 1450(134.71 sq.m.)built up area: 1400 sq.ft. (130.06 sq.m.)carpet area: 1000 sq.ft. (92.9 sq.m.)
3rd rowsuper built up area 1342(124.68 sq.m.)carpet area: 810 sq.ft. (75.25 sq.m.)
4th rowcarpet area: 366.08 (34.01 sq.m.)
5th rowsuper built up area 1950(181.16 sq.m.)
ValueCountFrequency (%)
area 5598
18.5%
sq.m 3677
12.2%
up 3024
 
10.0%
built 2320
 
7.7%
super 1876
 
6.2%
sq.ft 1754
 
5.8%
sq.m.)carpet 1188
 
3.9%
sq.m.)built 702
 
2.3%
plot 696
 
2.3%
carpet 686
 
2.3%
Other values (2858) 8734
28.9%
2025-04-04T06:21:29.849351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26556
 
13.3%
. 20471
 
10.2%
a 13210
 
6.6%
r 9488
 
4.7%
s 9471
 
4.7%
e 9352
 
4.7%
1 9231
 
4.6%
u 7924
 
4.0%
p 7474
 
3.7%
q 7459
 
3.7%
Other values (22) 79527
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91667
45.8%
Decimal Number 47323
23.6%
Space Separator 26556
 
13.3%
Other Punctuation 23497
 
11.7%
Close Punctuation 5560
 
2.8%
Open Punctuation 5560
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13210
14.4%
r 9488
10.4%
s 9471
10.3%
e 9352
10.2%
u 7924
8.6%
p 7474
8.2%
q 7459
8.1%
t 7352
8.0%
m 5569
6.1%
l 3720
 
4.1%
Other values (7) 10648
11.6%
Decimal Number
ValueCountFrequency (%)
1 9231
19.5%
0 6658
14.1%
2 5700
12.0%
5 4736
10.0%
3 3978
8.4%
4 3738
7.9%
6 3694
7.8%
7 3262
 
6.9%
8 3174
 
6.7%
9 3152
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 20471
87.1%
: 3026
 
12.9%
Space Separator
ValueCountFrequency (%)
26556
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5560
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5560
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108496
54.2%
Latin 91667
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13210
14.4%
r 9488
10.4%
s 9471
10.3%
e 9352
10.2%
u 7924
8.6%
p 7474
8.2%
q 7459
8.1%
t 7352
8.0%
m 5569
6.1%
l 3720
 
4.1%
Other values (7) 10648
11.6%
Common
ValueCountFrequency (%)
26556
24.5%
. 20471
18.9%
1 9231
 
8.5%
0 6658
 
6.1%
2 5700
 
5.3%
) 5560
 
5.1%
( 5560
 
5.1%
5 4736
 
4.4%
3 3978
 
3.7%
4 3738
 
3.4%
Other values (5) 16308
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26556
 
13.3%
. 20471
 
10.2%
a 13210
 
6.6%
r 9488
 
4.7%
s 9471
 
4.7%
e 9352
 
4.7%
1 9231
 
4.6%
u 7924
 
4.0%
p 7474
 
3.7%
q 7459
 
3.7%
Other values (22) 79527
39.7%

bedRoom
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.368478
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-04-04T06:21:29.953653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9706311
Coefficient of variation (CV)0.58502122
Kurtosis35.447912
Mean3.368478
Median Absolute Deviation (MAD)1
Skewness4.3110255
Sum12460
Variance3.8833868
MonotonicityNot monotonic
2025-04-04T06:21:30.048264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 1501
40.6%
2 947
25.6%
4 663
17.9%
5 211
 
5.7%
1 127
 
3.4%
6 78
 
2.1%
9 41
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (10) 45
 
1.2%
ValueCountFrequency (%)
1 127
 
3.4%
2 947
25.6%
3 1501
40.6%
4 663
17.9%
5 211
 
5.7%
6 78
 
2.1%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
36 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%

bathroom
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4298459
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-04-04T06:21:30.151208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.018743
Coefficient of variation (CV)0.58858124
Kurtosis33.034675
Mean3.4298459
Median Absolute Deviation (MAD)1
Skewness4.0305111
Sum12687
Variance4.0753232
MonotonicityNot monotonic
2025-04-04T06:21:30.246685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 1081
29.2%
2 1054
28.5%
4 824
22.3%
5 294
 
7.9%
1 160
 
4.3%
6 118
 
3.2%
7 41
 
1.1%
9 41
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (10) 39
 
1.1%
ValueCountFrequency (%)
1 160
 
4.3%
2 1054
28.5%
3 1081
29.2%
4 824
22.3%
5 294
 
7.9%
6 118
 
3.2%
7 41
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
36 1
 
< 0.1%
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size239.6 KiB
3+
1176 
3
1077 
2
891 
1
373 
0
182 

Length

Max length2
Median length1
Mean length1.3179238
Min length1

Characters and Unicode

Total characters4875
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3+
2nd row3
3rd row2
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3+ 1176
31.8%
3 1077
29.1%
2 891
24.1%
1 373
 
10.1%
0 182
 
4.9%

Length

2025-04-04T06:21:30.352466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T06:21:30.428343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 2253
60.9%
2 891
 
24.1%
1 373
 
10.1%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2253
46.2%
+ 1176
24.1%
2 891
 
18.3%
1 373
 
7.7%
0 182
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3699
75.9%
Math Symbol 1176
 
24.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2253
60.9%
2 891
 
24.1%
1 373
 
10.1%
0 182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4875
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2253
46.2%
+ 1176
24.1%
2 891
 
18.3%
1 373
 
7.7%
0 182
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2253
46.2%
+ 1176
24.1%
2 891
 
18.3%
1 373
 
7.7%
0 182
 
3.7%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.2%
Missing20
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.6985594
Minimum-1
Maximum45
Zeros130
Zeros (%)3.5%
Negative3
Negative (%)0.1%
Memory size57.8 KiB
2025-04-04T06:21:30.540541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum45
Range46
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.9304631
Coefficient of variation (CV)0.8853341
Kurtosis4.0100842
Mean6.6985594
Median Absolute Deviation (MAD)3
Skewness1.640777
Sum24644
Variance35.170392
MonotonicityNot monotonic
2025-04-04T06:21:30.665954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 506
13.7%
2 502
13.6%
1 364
 
9.8%
4 318
 
8.6%
8 195
 
5.3%
6 183
 
4.9%
10 177
 
4.8%
7 176
 
4.8%
5 170
 
4.6%
9 161
 
4.4%
Other values (33) 927
25.1%
ValueCountFrequency (%)
-1 3
 
0.1%
0 130
 
3.5%
1 364
9.8%
2 502
13.6%
3 506
13.7%
4 318
8.6%
5 170
 
4.6%
6 183
 
4.9%
7 176
 
4.8%
8 195
 
5.3%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 3
0.1%
32 2
0.1%

facing
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.3%
Missing1053
Missing (%)28.5%
Memory size259.7 KiB
East
630 
North-East
624 
North
389 
West
249 
South
232 
Other values (3)
522 

Length

Max length10
Median length5
Mean length6.8333333
Min length4

Characters and Unicode

Total characters18081
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth-East
2nd rowEast
3rd rowNorth-West
4th rowWest
5th rowEast

Common Values

ValueCountFrequency (%)
East 630
17.0%
North-East 624
16.9%
North 389
 
10.5%
West 249
 
6.7%
South 232
 
6.3%
North-West 194
 
5.2%
South-East 174
 
4.7%
South-West 154
 
4.2%
(Missing) 1053
28.5%

Length

2025-04-04T06:21:30.804963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T06:21:30.900430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
east 630
23.8%
north-east 624
23.6%
north 389
14.7%
west 249
 
9.4%
south 232
 
8.8%
north-west 194
 
7.3%
south-east 174
 
6.6%
south-west 154
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3792
21.0%
s 2025
11.2%
o 1767
9.8%
h 1767
9.8%
E 1428
 
7.9%
a 1428
 
7.9%
N 1207
 
6.7%
r 1207
 
6.7%
- 1146
 
6.3%
W 597
 
3.3%
Other values (3) 1717
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13143
72.7%
Uppercase Letter 3792
 
21.0%
Dash Punctuation 1146
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3792
28.9%
s 2025
15.4%
o 1767
13.4%
h 1767
13.4%
a 1428
 
10.9%
r 1207
 
9.2%
e 597
 
4.5%
u 560
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1428
37.7%
N 1207
31.8%
W 597
15.7%
S 560
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16935
93.7%
Common 1146
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3792
22.4%
s 2025
12.0%
o 1767
10.4%
h 1767
10.4%
E 1428
 
8.4%
a 1428
 
8.4%
N 1207
 
7.1%
r 1207
 
7.1%
W 597
 
3.5%
e 597
 
3.5%
Other values (2) 1120
 
6.6%
Common
ValueCountFrequency (%)
- 1146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18081
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3792
21.0%
s 2025
11.2%
o 1767
9.8%
h 1767
9.8%
E 1428
 
7.9%
a 1428
 
7.9%
N 1207
 
6.7%
r 1207
 
6.7%
- 1146
 
6.3%
W 597
 
3.3%
Other values (3) 1717
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size283.1 KiB
Relatively New
1651 
New Property
594 
Moderately Old
569 
Undefined
310 
Old Property
309 

Length

Max length18
Median length14
Mean length13.380373
Min length9

Characters and Unicode

Total characters49494
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOld Property
2nd rowNew Property
3rd rowRelatively New
4th rowRelatively New
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New 1651
44.6%
New Property 594
 
16.1%
Moderately Old 569
 
15.4%
Undefined 310
 
8.4%
Old Property 309
 
8.4%
Under Construction 266
 
7.2%

Length

2025-04-04T06:21:31.019921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T06:21:31.105203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
new 2245
31.7%
relatively 1651
23.3%
property 903
12.7%
old 878
 
12.4%
moderately 569
 
8.0%
undefined 310
 
4.4%
under 266
 
3.8%
construction 266
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8474
17.1%
l 4749
 
9.6%
t 3655
 
7.4%
3389
 
6.8%
y 3123
 
6.3%
r 2907
 
5.9%
d 2333
 
4.7%
N 2245
 
4.5%
w 2245
 
4.5%
i 2227
 
4.5%
Other values (15) 14147
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39017
78.8%
Uppercase Letter 7088
 
14.3%
Space Separator 3389
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8474
21.7%
l 4749
12.2%
t 3655
9.4%
y 3123
 
8.0%
r 2907
 
7.5%
d 2333
 
6.0%
w 2245
 
5.8%
i 2227
 
5.7%
a 2220
 
5.7%
o 2004
 
5.1%
Other values (7) 5080
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 2245
31.7%
R 1651
23.3%
P 903
12.7%
O 878
 
12.4%
U 576
 
8.1%
M 569
 
8.0%
C 266
 
3.8%
Space Separator
ValueCountFrequency (%)
3389
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46105
93.2%
Common 3389
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8474
18.4%
l 4749
 
10.3%
t 3655
 
7.9%
y 3123
 
6.8%
r 2907
 
6.3%
d 2333
 
5.1%
N 2245
 
4.9%
w 2245
 
4.9%
i 2227
 
4.8%
a 2220
 
4.8%
Other values (14) 11927
25.9%
Common
ValueCountFrequency (%)
3389
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8474
17.1%
l 4749
 
9.6%
t 3655
 
7.4%
3389
 
6.8%
y 3123
 
6.3%
r 2907
 
5.9%
d 2333
 
4.7%
N 2245
 
4.5%
w 2245
 
4.5%
i 2227
 
4.5%
Other values (15) 14147
28.6%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct594
Distinct (%)31.7%
Missing1823
Missing (%)49.3%
Infinite0
Infinite (%)0.0%
Mean1924.5232
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-04-04T06:21:31.243763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile762.75
Q11478.75
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)736.25

Descriptive statistics

Standard deviation764.59476
Coefficient of variation (CV)0.39729049
Kurtosis10.324475
Mean1924.5232
Median Absolute Deviation (MAD)372
Skewness1.8323647
Sum3610405.5
Variance584605.15
MonotonicityNot monotonic
2025-04-04T06:21:31.383562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
2408 19
 
0.5%
1900 19
 
0.5%
1930 18
 
0.5%
1350 17
 
0.5%
Other values (584) 1635
44.2%
(Missing) 1823
49.3%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct648
Distinct (%)38.0%
Missing1992
Missing (%)53.9%
Infinite0
Infinite (%)0.0%
Mean2383.3548
Minimum14
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-04-04T06:21:31.545437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile240.3
Q11100
median1650
Q32400
95-th percentile4694
Maximum737147
Range737133
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17863.018
Coefficient of variation (CV)7.4949049
Kurtosis1680.9102
Mean2383.3548
Median Absolute Deviation (MAD)650
Skewness40.844131
Sum4068386.6
Variance3.190874 × 108
MonotonicityNot monotonic
2025-04-04T06:21:31.771125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 38
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 33
 
0.9%
900 31
 
0.8%
1600 26
 
0.7%
2000 24
 
0.6%
1300 24
 
0.6%
1700 23
 
0.6%
Other values (638) 1400
37.8%
(Missing) 1992
53.9%
ValueCountFrequency (%)
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
45 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
56.6 1
 
< 0.1%
57 1
 
< 0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
26000 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct735
Distinct (%)39.1%
Missing1821
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean2524.9915
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-04-04T06:21:31.977880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1835.47
median1300
Q31788.75
95-th percentile2957.5
Maximum607936
Range607921
Interquartile range (IQR)953.28

Descriptive statistics

Standard deviation22763.57
Coefficient of variation (CV)9.0153057
Kurtosis606.47584
Mean2524.9915
Median Absolute Deviation (MAD)475
Skewness24.372037
Sum4741934
Variance5.1818013 × 108
MonotonicityNot monotonic
2025-04-04T06:21:32.633812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1600 35
 
0.9%
1800 35
 
0.9%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1000 22
 
0.6%
1450 22
 
0.6%
Other values (725) 1584
42.8%
(Missing) 1821
49.2%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.4 KiB
0
2990 
1
709 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3699
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2990
80.8%
1 709
 
19.2%

Length

2025-04-04T06:21:32.804283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T06:21:32.898055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2990
80.8%
1 709
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2990
80.8%
1 709
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3699
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2990
80.8%
1 709
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2990
80.8%
1 709
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2990
80.8%
1 709
 
19.2%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.4 KiB
0
2365 
1
1334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3699
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2365
63.9%
1 1334
36.1%

Length

2025-04-04T06:21:33.010846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T06:21:33.125118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2365
63.9%
1 1334
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2365
63.9%
1 1334
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3699
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2365
63.9%
1 1334
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2365
63.9%
1 1334
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2365
63.9%
1 1334
36.1%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.4 KiB
0
3358 
1
341 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3699
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3358
90.8%
1 341
 
9.2%

Length

2025-04-04T06:21:33.255717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T06:21:33.349529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3358
90.8%
1 341
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3358
90.8%
1 341
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3699
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3358
90.8%
1 341
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3358
90.8%
1 341
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3358
90.8%
1 341
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.4 KiB
0
3039 
1
660 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3699
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3039
82.2%
1 660
 
17.8%

Length

2025-04-04T06:21:33.470880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T06:21:33.581631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3039
82.2%
1 660
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3039
82.2%
1 660
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3699
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3039
82.2%
1 660
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3039
82.2%
1 660
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3039
82.2%
1 660
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.4 KiB
0
3291 
1
408 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3699
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3291
89.0%
1 408
 
11.0%

Length

2025-04-04T06:21:33.706794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T06:21:33.815295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3291
89.0%
1 408
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3291
89.0%
1 408
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3699
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3291
89.0%
1 408
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3291
89.0%
1 408
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3291
89.0%
1 408
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.4 KiB
0
2703 
1
798 
2
 
198

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3699
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2703
73.1%
1 798
 
21.6%
2 198
 
5.4%

Length

2025-04-04T06:21:33.941647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T06:21:34.008312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2703
73.1%
1 798
 
21.6%
2 198
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 2703
73.1%
1 798
 
21.6%
2 198
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3699
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2703
73.1%
1 798
 
21.6%
2 198
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2703
73.1%
1 798
 
21.6%
2 198
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2703
73.1%
1 798
 
21.6%
2 198
 
5.4%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.206542
Minimum0
Maximum174
Zeros470
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-04-04T06:21:34.109269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median58
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.098789
Coefficient of variation (CV)0.74570099
Kurtosis-0.87731394
Mean71.206542
Median Absolute Deviation (MAD)37
Skewness0.46428382
Sum263393
Variance2819.4814
MonotonicityNot monotonic
2025-04-04T06:21:34.238623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 470
 
12.7%
49 349
 
9.4%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
15 45
 
1.2%
37 45
 
1.2%
Other values (151) 2326
62.9%
ValueCountFrequency (%)
0 470
12.7%
5 6
 
0.2%
6 6
 
0.2%
7 42
 
1.1%
8 31
 
0.8%
9 10
 
0.3%
12 7
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 45
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-04-04T06:21:24.388077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:13.119686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:14.199158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:15.488406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:16.520061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:17.605861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:19.501513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:20.987869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:22.236394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:23.322628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:24.500277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:13.236374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:14.303120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:15.586691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:16.624455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:17.713991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:19.650013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:21.158700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:22.348673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:23.424082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:24.619851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:13.340738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:14.409763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:15.688296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:16.735715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:17.819443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:19.798563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:21.321333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:22.460092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:23.544700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:25.038127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:13.437735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:14.511752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:15.794217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:16.831117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:17.925780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:19.937754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:21.484034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:22.573914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:23.648876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:25.160678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:13.543009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:14.829143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:15.891775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:16.933031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:18.050832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:20.081590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:21.600253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:22.693467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:23.760090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:25.274480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:13.649834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:14.938017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:15.993649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:17.048438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:18.237473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:20.226054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:21.704827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:22.807870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:23.874187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:25.382032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:13.749467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:15.037275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:16.088205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:17.156423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:18.392771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:20.355193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:21.805327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:22.909971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:23.975842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:25.492995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:13.853710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:15.160446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:16.203041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:17.274223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:18.557649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:20.513107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:21.901790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:22.998973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:24.073470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:25.619084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:13.972249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:15.267231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:16.308136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:17.384880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:18.761103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:20.678587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:22.015203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:23.116154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:24.175988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:25.733041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:14.073706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:15.374223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:16.413054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:17.494913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:19.341473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:20.838033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:22.125628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:23.210020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T06:21:24.278565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-04T06:21:34.361596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2720.0790.1180.0000.0000.0910.1380.1850.2550.1090.1870.1010.0590.3840.2850.1440.1410.086
area0.0001.0000.0100.6850.6210.8380.8020.0220.1090.0420.2620.0420.0370.7440.2030.0280.0150.0380.0180.948
balcony0.2720.0101.0000.1630.1150.0000.0250.0190.0970.2000.2230.0810.1960.1370.0330.2110.4410.1450.1840.307
bathroom0.0790.6850.1631.0000.8610.4660.5990.035-0.0100.1970.1820.0720.2650.7200.4110.4430.3110.1730.1600.819
bedRoom0.1180.6210.1150.8611.0000.3780.5680.038-0.1100.1680.0580.0710.2680.6800.4170.6200.1410.2000.1660.800
built_up_area0.0000.8380.0000.4660.3781.0000.9691.0000.0790.0890.2940.0000.0000.6040.1240.0000.0000.0000.0000.926
carpet_area0.0000.8020.0250.5990.5680.9691.0000.0000.1560.0000.2400.0160.0000.6130.1370.0000.0000.0000.0040.894
facing0.0910.0220.0190.0350.0381.0000.0001.0000.0000.0650.0650.0000.0240.0210.0000.0940.0340.0350.0000.000
floorNum0.1380.1090.097-0.010-0.1100.0790.1560.0001.0000.0440.2430.0170.088-0.002-0.1220.4910.1030.1130.0570.153
furnishing_type0.1850.0420.2000.1970.1680.0890.0000.0650.0441.0000.2190.0660.2210.2190.0170.1420.3010.1550.1480.171
luxury_score0.2550.2620.2230.1820.0580.2940.2400.0650.2430.2191.0000.1760.1920.2190.0560.3320.3470.2280.1850.223
others0.1090.0420.0810.0720.0710.0000.0160.0000.0170.0660.1761.0000.0340.0340.0350.0260.0000.1060.0320.085
pooja room0.1870.0370.1960.2650.2680.0000.0000.0240.0880.2210.1920.0341.0000.3320.0360.2510.2530.3040.3150.157
price0.1010.7440.1370.7200.6800.6040.6130.021-0.0020.2190.2190.0340.3321.0000.7420.5380.3700.3010.2440.773
price_per_sqft0.0590.2030.0330.4110.4170.1240.1370.000-0.1220.0170.0560.0350.0360.7421.0000.2030.0420.0000.0310.288
property_type0.3840.0280.2110.4430.6200.0000.0000.0940.4910.1420.3320.0260.2510.5380.2031.0000.0650.2410.1281.000
servant room0.2850.0150.4410.3110.1410.0000.0000.0340.1030.3010.3470.0000.2530.3700.0420.0651.0000.1600.1860.584
store room0.1440.0380.1450.1730.2000.0000.0000.0350.1130.1550.2280.1060.3040.3010.0000.2410.1601.0000.2250.046
study room0.1410.0180.1840.1600.1660.0000.0040.0000.0570.1480.1850.0320.3150.2440.0310.1280.1860.2251.0000.121
super_built_up_area0.0860.9480.3070.8190.8000.9260.8940.0000.1530.1710.2230.0850.1570.7730.2881.0000.5840.0460.1211.000

Missing values

2025-04-04T06:21:25.921947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-04T06:21:26.139456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-04T06:21:26.397645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatambience lagoonsector 244.2513281.03200.0super built up area 3200(297.29 sq.m.)carpet area: 3156 sq.ft. (293.2 sq.m.)343+3.0North-EastOld Property3200.0NaN3156.000000010101143
1flatcapital residences 360sector 701.188137.01450.0super built up area 1450(134.71 sq.m.)built up area: 1400 sq.ft. (130.06 sq.m.)carpet area: 1000 sq.ft. (92.9 sq.m.)2237.0EastNew Property1450.01400.01000.000000000100140
2flatumang winter hillssector 770.715305.01338.0super built up area 1342(124.68 sq.m.)carpet area: 810 sq.ft. (75.25 sq.m.)2226.0North-WestRelatively New1342.0NaN810.000000100000108
3flatrof anandasector 950.205463.0366.0carpet area: 366.08 (34.01 sq.m.)11110.0WestRelatively NewNaNNaN366.08023900000082
4flatshree vardhman florasector 900.904615.01950.0super built up area 1950(181.16 sq.m.)3434.0EastRelatively New1950.0NaNNaN010000165
5flatpareena mi casasector 681.109020.01220.0super built up area 1245(115.66 sq.m.)carpet area: 1225 sq.ft. (113.81 sq.m.)22313.0EastNew Property1245.0NaN1225.000000000000106
6flatsahara gracesector 285.5014612.03764.0super built up area 3764(349.69 sq.m.)453+5.0EastModerately Old3764.0NaNNaN0100118
7flatsidhartha ncr greenssector 950.966201.01548.0carpet area: 1548 (143.81 sq.m.)3338.0NaNNew PropertyNaNNaN1548.0000000011008
8flatireo victory valleysector 672.9512110.02436.0super built up area 2436(226.31 sq.m.)built up area: 2236 sq.ft. (207.73 sq.m.)carpet area: 2036 sq.ft. (189.15 sq.m.)333+14.0SouthModerately Old2436.02236.02036.000000010101165
9flatshyam apartmentsector 50.263714.0700.0super built up area 700(65.03 sq.m.)carpet area: 530 sq.ft. (49.24 sq.m.)2122.0NaNRelatively New700.0NaN530.0000000000000
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3814flatsare crescent parcsector 920.804210.01900.0super built up area 1900(176.52 sq.m.)43210.0EastRelatively New1900.0NaNNaN00001037
3815flathsiidc sidco shivalik apartmentsmanesar0.855102.01666.0super built up area 1666(154.78 sq.m.)3225.0NaNModerately Old1666.0NaNNaN0000000
3816flateldeco accoladesohna0.715607.01266.0super built up area 1275(118.45 sq.m.)22310.0North-EastRelatively New1275.0NaNNaN000000159
3817flatramsons kshitijsector 950.19591.03215.0carpet area: 3212 (298.4 sq.m.)11114.0EastRelatively NewNaNNaN3212.000000249
3818flatm3m merlinsector 674.5014267.03154.0super built up area 3154(293.02 sq.m.)443+15.0South-WestRelatively New3154.0NaNNaN010001158
3819flatdlf new town heightssector 901.546514.02364.0super built up area 2364(219.62 sq.m.)built up area: 2200 sq.ft. (204.39 sq.m.)carpet area: 1850 sq.ft. (171.87 sq.m.)443+12.0South-WestModerately Old2364.02200.01850.0010100111
3821flatdlf regal gardenssector 901.267403.01702.0super built up area 1702(158.12 sq.m.)carpet area: 1500 sq.ft. (139.35 sq.m.)3336.0North-EastRelatively New1702.0NaN1500.000001128
3822flatunitech the residencessector 331.559872.01570.0super built up area 1570(145.86 sq.m.)3411.0SouthRelatively New1570.0NaNNaN01000037
3823houseindependentsector 91.1055000.0200.0plot area 200(18.58 sq.m.)4312.0North-EastOld PropertyNaN200.0NaN0000000
3824flatshapoorji pallonji joyville gurugramsector 1021.9510529.01852.0super built up area 1852(172.06 sq.m.)33310.0North-EastRelatively New1852.0NaNNaN00000059